Conventional image editing systems allow users to manually edit images by manipulating features. For example, conventional image editing systems allow users to adjust values of contrast, vibrance, saturation etc. of a digital image. For many users, particularly inexperienced users, adjusting feature values is not intuitive. For example, the users do not know how to adjust features values to achieve a desired image edit. As a result, such conventional image editing systems often require inexperienced users to edit images through a trial and error process (e.g., an iterative process).
Some image editing systems, in an effort to address the above-described problem, provide an automatic image edits. However, such image editing systems typically apply the same generic edit to the digital image regardless of the content of the actual image or the preferences of the user. Such “one size fits all” image edits often are unsatisfactory.
As such, conventional automatic image edits may not reflect how a user would edit an image. In particular, conventional automatic image edits are typically not personalized to the user or to an editing level (e.g., category) of the user. Thus, often images to which automatic image edits are applied have the same general feel.
Accordingly, these and other disadvantages exist with respect to conventional image editing systems.